Anti-forensic Analysis for Image Splicing Detection Through Advanced Filters

Nowadays, manipulated images can be found everywhere due to the wide availability of sophisticated image editing tools. As the digital technology advances for multimedia, the field of image forensic has also made significant progress in recent years with the development of effective and more advanced image tampering detection methods. Furthermore, there has been continuous development in the image tampering methods along with the techniques used for hiding tampering clues aiming to evade the existing forensic approaches. Image splicing is one of the most common and simple image tampering approach, where an object or region is copied from one image and stitched into the other image. This paper presents an anti-forensic analysis on three advanced filters: the weighted average filter, bilateral blur filter, and Kuwahara filter. The objective of this analysis is to examine the impact of filtering operations on the detection accuracy of image splicing. Two deep learning-based image splicing detection models are proposed which are initialized with pre-trained weights of ResNet and InceptionNet architecture. To assess the effect of filtering, the structural similarity index measure (SSIM) is employed to quantify the similarity between images before and after the application of the filters. Based on the experimental results, the weighted average filter and Kuwahara filter emerge as the most effective anti-forensic attack on spliced images. These filters demonstrate the ability to preserve the SSIM of the image while inducing a substantial decrease in the accuracy of splicing detection.